Abstract
This study identified and characterized the factors influencing managers' intentions to adopt collaborative robots (cobots) in manufacturing companies. Based on a conceptual framework that integrates three technology adoption theories (Diffusion of Innovation, Technology-organization-environment and Institutional theory) and following an exploratory qualitative research design, this paper identifies 39 factors influencing the intention to adopt cobots in three contexts (internal, external and technology). Twelve of these factors are new as contrasted with previous literature. The findings of this study can assist organizations in their process of adoption of cobots and in the development of managerial practices that consider the role of these factors.

Abstract
Purpose To study how supply chain decision makers gather, process and use the available internal and external information when facing supply chain disruptions. Design/methodology/approach The paper reviews relevant supply chain literature to build an information management model for disruption management. Afterwards, three case studies in the vehicle assembly sector, namely cars, trucks and aircraft wings, bring the empirical insights to the information management model. Findings This research characterises the phases of disruption management and identifies the information companies use to recover from a variety of disruptive events. It presents an information management model to enhance supply chain visibility and support disruption management at the operational level. Moreover, it arrives at two design propositions to help companies in the redesign of their disruption discovery and recovery processes. Originality/value This research studies how companies manage operational disruptions. The proposed information management model allows to provide visibility to support the disruption management process. Also, based on the analysis of the disruptions occurring at the operational level we propose a conceptual model to support decision makers in the recovery from daily disruptive events.

Abstract
The automotive industry is one of the most demanding sectors of the global market. The response capacity and flexibility of companies represent a key factor for their success. Applying Six Sigma, it was carried out an improvement project aiming at reducing the quantity of scrap on the most critical sector of a automotive components’ manufacturer achieving a better comprehension of the flows, process characteristics and different variables associated to the scrap generation, identifying the equipment responsible for that scrap and its type. Brain- storming sessions were performed, as well as the application of 5 Why’s and 5W2H techniques in order to fulfill the Ishikawa diagrams aiming at understanding possible root-causes for the scrap generation. A definition of the improvement actions has been developed. A reduction of 15% was achieve just in the machine identified as the main generator of scrap in these processes. © 2020, Springer Nature Switzerland AG.

Abstract

Abstract
The Industry 4.0 (i4.0) paradigm was conceived bearing smart machines enabling capabilities, mostly through real-time communication both between smart equipment on a shop floor and decision-aiding software at the business level. This interoperability is achieved mostly through a reference architecture specifically designed for i4.0, which is aimed at devising the information architecture with real-time capabilities. From such architectures, the Reference Architectural Model for Industrie 4.0 (RAMI 4.0) is considered the preferred approach for implementation purposes, especially within Small and Medium Enterprises (SMEs). Nevertheless, the implementation of RAMI 4.0 is surrounded with great challenges when considering the current industrial landscape, which requires retrofitting of existing equipment and the various communication needs. Through three different case studies conducted within footwear and cork industries, this research proposes a RAMI 4.0 SME implementation methodology that considers the initial stages of equipment preparation to enable smart communications and capabilities. The result is a methodological route aimed for SMEs’ implementation of smart machines, based on RAMI 4.0, which considers both the technological aspects as well as the business requirements. © 2020, Springer Nature Switzerland AG.

Abstract
Sensors are susceptible to failure when exposed to extreme conditions over long periods of time. Besides they can be affected by noise or electrical interference. Models (Machine Learning or others) obtained from these faulty and noisy sensors may be less reliable. In this paper, we propose a data augmentation approach for making neural networks more robust to missing and faulty sensor data. This approach is shown to be effective in a real life industrial application that uses data of various sensors to predict the wear of an automotive fuel-system component. Empirical results show that the proposed approach leads to more robust neural network in this particular application than existing methods.